Hit Discovery, Discovery Sciences, R&D, AstraZeneca, Alderley Park UK.
Data Sciences and Quantitative Biology, Discovery Sciences, R&D, AstraZeneca, Cambridge, UK.
SLAS Discov. 2021 Feb;26(2):248-256. doi: 10.1177/2472555220983809.
Enzymes represent a significant proportion of the druggable genome and constitute a rich source of drug targets. Delivery of a successful program for developing a modulator of enzyme activity requires an understanding of the enzyme's mechanism and the mode of interaction of compounds. This allows an understanding of how physiological conditions in disease-relevant cells will affect inhibitor potency. As a result, there is increasing interest in evaluating hit compounds from high-throughput screens to determine their mode of interaction with the target. This work revisits the common inhibition modalities and illustrates the impact of substrate concentration relative to K upon the pattern of changes in IC that are expected for increasing substrate concentration. It proposes a new, high-throughput approach for assessing mode of inhibition, incorporating analyses based on a minimal descriptive model, to deliver a workflow that allows rapid and earlier compound classification immediately after high-throughput screening.
酶代表了可成药基因组的重要部分，也是药物靶点的丰富来源。成功开发一种酶活性调节剂的项目需要对酶的机制和化合物的相互作用模式有深入的了解。这可以帮助理解疾病相关细胞中的生理条件将如何影响抑制剂的效力。因此，人们越来越有兴趣评估高通量筛选中的命中化合物，以确定它们与靶标的相互作用模式。这项工作重新审视了常见的抑制模式，并说明了相对于 K 时底物浓度对预期随底物浓度增加而变化的 IC 模式的影响。它提出了一种新的高通量抑制模式评估方法，该方法基于一个最小描述模型进行分析，提供了一种工作流程，允许在高通量筛选后立即快速和早期对化合物进行分类。
